1.最短路(Floyd、Dijstra,BellmanFord) 2.最小生成树(先写个prim,kruscal要用并查集,不好写) 3.大数(高精度)加减乘除 4.二分查找. (代码可在五行以内) 5.叉乘、判线段相交、然后写个凸包. 6.BFS、DFS,同时熟练hash表(要熟,要灵活,代码要简) 7.数学上的有:辗转相除(两行内),线段交点、多角形面积公式.
It's John Sonmez from simpleprogrammer.com. I have a question here that a lot of people have been asking lately since I did this video on “Do I need to learn algorithms?” Now, the question is “How to learn algorithms?” Obviously, that was—I must have known that was coming. Thi...
Because of this discovery, it is possible than even faster algorithms will be discovered. It is therefore natural to ask: did fast human calculators of the past use faster algorithms – in which case we can learn from their experience – or they simply performed all operations within a ...
But there's growing research that pricing algorithms themselves could learn to form a kind of digital cartel of their own… and collude to raise prices without any human involvement. Joseph Harrington:Now, well let's think about a manager deciding that they're going to delegate the pricing dec...
Social media companies’ drive to keep you on their platforms clashes with how people evolved to learn from each other
You can’t use machine learning unless you know how to program. Luckily, we have a free guide:How to Learn Python for Data Science, The Self-Starter Way Statistics for Data Science Statistics, especially Bayesian probability, underpins many ML algorithms. We have a free guide:How to Learn ...
Overproduction: Producing features that nobody is going to use. Over-processing: Unnecessary complex algorithms solving simple problems. Defects: Bugs. 7 Wastes in Marketing Transportation: Task switching, interruptions, unnecessary long marketing funnel. Inventory: Fully-prepared marketing campaigns which ...
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We don’t know what the function (f) looks like or it’s form. If we did, we would use it directly and we would not need to learn it from data using machine learning algorithms. It is harder than you think. There is also error (e) that is independent of the input data (X). ...
The power of machine learning comes from its ability to learn from data and apply that learning experience to new data that a system has never seen before. However, one of the challenges data scientists have is ensuring the data fed into machine learning algorithms is not only clean, accurate...